Rotation-Invariant Feature Learning in VHR Optical Remote Sensing Images via Nested Siamese Structure With Double Center Loss

Jiang, Ruoqiao; Mei, Shaohui; Ma, Mingyang; Zhang, Shun

doi:10.1109/tgrs.2020.3021283

Cited by 12 publications

(4 citation statements)

References 35 publications

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“…It has also been widely used in remote sensing image scene classification [46]- [50]. A deep learning model composed of multiple processing layers can learn more powerful features, such as rotation-invariant feature learning [51], spatial-spectral feature learning [52], and multimodal feature learning [53]. Deep learning-based methods have also achieved impressive results and state-of-the-art performance in remote sensing image scene classification [12]- [15].…”

Section: A Feature Representation For Remote Sensing Imagementioning

confidence: 99%

Fusing Deep Features by Kernel Collaborative Representation for Remote Sensing Scene Classification

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: A Feature Representation For Remote Sensing Imagementioning

confidence: 99%

Fusing Deep Features by Kernel Collaborative Representation for Remote Sensing Scene Classification

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…These models include Spatial Transformer Network (STN) [14], Polar Transformer Network [15], Oriented Response Network (ORN) [16], Rotation-Invariant Coordinate CNN (RIC-CNN) [17], and so on [18], [19], [20], [21]. Although they have been used in different practical tasks [22], [23], [24], [25], [26], [27], these existing rotation-invariant/equivariant CNNs have three major limitations: 1) Most methods are invariant to specific rotation angles rather than arbitrary angles [9], [16], [20]. Some of them, like RIC-CNN [17], are only invariant to rotations around image center.…”

Section: Introductionmentioning

confidence: 99%

Sorting Convolution Operation for Achieving Rotational Invariance

Mo,

Zhao

2024

IEEE Signal Process. Lett.

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The topic of achieving rotational invariance in convolutional neural networks (CNNs) has gained considerable attention recently, as this invariance is crucial for many computer vision tasks. In this letter, we propose a sorting convolution operation (SConv), which achieves invariance to arbitrary rotations without additional learnable parameters or data augmentation. It can directly replace conventional convolution operations in a classic CNN model to achieve the model's rotational invariance. Based on MNIST-rot dataset, we first analyze the impact of convolution kernel size, sampling grid and sorting method on SConv's rotational invariance, and compare our method with previous rotation-invariant CNN models. Then, we combine SConv with VGG, ResNet and DenseNet, and conduct classification experiments on texture and remote sensing image datasets. The results show that SConv significantly improves the performance of these models, especially when training data is limited. Our code can be downloaded from https://github.com/HanlinMo/Sorting-Convolution-Operation-for-Achieving-Rotational-Invariance.

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“…In recent years, optical remote sensing imaging technology has developed rapidly. Pleiades, WorldView‐3, Gaofen‐2, Gaofen‐1 and other optical remote sensing satellites have been launched successively, and they continuously provide a large number of high‐resolution images, which have brought new opportunities and challenges to the development of object detection technology (Jiang et al, 2021; Yin et al, 2020). The opportunity is that massive images provide important data sources for the development of object detection technology.…”

Section: Introductionmentioning

confidence: 99%

A novel deep learning‐based single shot multibox detector model for object detection in optical remote sensing images

Wang

Yin

Alyami

et al. 2022

Geoscience Data Journal

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Remote sensing image object detection is widely used in civil and military fields. The important task is to detect objects such as ships, planes, airports, harbours and so on, and then it can obtain object category and position information. It is of great significance to use remote sensing images to observe the densely arranged and directional targets such as cars and ships parked in parking lots and harbours. The object detection task mainly includes object localization and classification. Remote sensing images contain large number of small objects and dense scenes due to the long shooting distance and wide coverage. Small objects occupy few pixels in the image, and they are easily miss‐detected. In dense scenes, the overlapping part of each object is large, so it is easy to detect objects repeatedly. The traditional small object detection methods deliver low accuracy and take long time. Therefore, object detection is very challenging. We put forward a novel deep learning‐based single shot multibox detector (SSD) model for object detection. First, we propose an improved inception network to optimize SSD to strengthen the small object feature extraction ability (FEA) in the shallow network. Second, the feature pyramid network is modified to enhance the fusion effect. Third, the deep feature fusion module is designed to improve the FEA of the deep network. Finally, the extracted image features are matched with candidate boxes with different aspect ratios to perform object detection and location with different scales. Experiments on DOTA show that the proposed algorithm can adapt to the remote sensing object detection in different backgrounds, and effectively improve the detection effect of remote sensing objects in complex scenes.

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Rotation-Invariant Feature Learning in VHR Optical Remote Sensing Images via Nested Siamese Structure With Double Center Loss

Cited by 12 publications

References 35 publications

Fusing Deep Features by Kernel Collaborative Representation for Remote Sensing Scene Classification

Fusing Deep Features by Kernel Collaborative Representation for Remote Sensing Scene Classification

Sorting Convolution Operation for Achieving Rotational Invariance

A novel deep learning‐based single shot multibox detector model for object detection in optical remote sensing images

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